Month: September 2020
MMS • Raul Salas
Snowflake’s Cloning and data masking is the answer to security restrictions related to health care related PHI data.
Snowflake has managed to solve issues that many database administrators have faced in corporate environments where they manage personal information. But this issue is magnified in the health care industry where protecting personal health data is the highest priority and makes even simple operational processes very difficult if not impossible to even consider.
For example, getting production data migrated and cleansed involves batch job/backup processes that involved moving data across servers over the network and then running programs to mask and scramble personal health info. So developers are left with stale data to work with even in their test environments.
Making changes to data that is incorrect in production is a labor and resource intensive process.
Snowflake’s cloning and data masking functionality will simplify many health care production environment processes as well as make them more secure. In this blogpost we will explore Snowflake’s impact on a typical Health care operational database and software development processes. NOTE: we will explore more in-depth Snowflake’s cloning and data masking technical implementation (Reference Architecture) in a future blogpost.
In short, here is what Snowflake cloning functionality can do.
1. Clone Very large databases/tables in seconds as many times as you want
2. Only pay for data you store and update (pay once) in other words you only pay for production original data store. If production used 2TB, you would only be charged for one copy of the data.
3. Update data in test automatically
4. Promote any test data to int or prod rapidly
5. Your cloned data will be updated in real time as production changes occur
6. You can go back to specific time or query and clone the database state at that time and create archived databases easily.
So cloning health data takes only a few seconds and your developers can start their work. In addition, changes to the clone that is made can easily be promoted in real-time to production. Think about all the hours and man power currently spent on making all the manual processes to support making copies of production data to lower environments.
Now, let’s combine this with Snowflake’s data masking and this is where things start to get really interesting.
1. Mask PHI field level data in production.
2. Clone Production to Test environment.
3. Apply security roles that will allow control over who sees Personal health information
4. Make changes to Test environment new changes to diagnostic codes for example.
5. Promote cloned test environment to Integration and ensure there are no issues with changes and data
6. Promote cloned integration environment to Production.
Snowflake’s new data masking combined with cloning will solves many issues in managing data environments as well as give DBAs the ability to sleep through the night and have a weekend to spend with their family!
Snowflake has really solved many issues that plague database operational and security issues in health care for the past 20 – 30 years!
MMS • Bruno Couriol
Article originally posted on InfoQ. Visit InfoQ
Eiji Kitamura recently addressed in a talk at Google’s web.dev live the new COOP and COEP policies that dictate how browsers handle cross-origin resources. The new opener (COOP) and embedded (COEP) policies set up a cross-origin isolated environment that protects against Spectre attacks while restoring powerful, previously disabled features (SharedArrayMemoryBuffer and more).
By Bruno Couriol
MMS • Dylan Schiemann
Article originally posted on InfoQ. Visit InfoQ
Cypress, a browser-based test runner and dashboard, recently introduced native support for test retries in the Cypress 5.0 release, helping developers avoid intermittent test failures. Other recent Cypress advances include networking stubbing and shadow DOM support.
By Dylan Schiemann
MMS • Anthony Alford
Article originally posted on InfoQ. Visit InfoQ
A team of scientists at Facebook AI Research have released a deep-learning model for processing protein data from DNA sequences. The model contains approximately 700M parameters, was trained on 250 million protein sequences, and learned representations of biological properties that can be used to improve current state-of-the-art in several genomics prediction tasks.
By Anthony Alford
MMS • Karthik Krishnaswamy
Article originally posted on InfoQ. Visit InfoQ
API calls now make up 83% of all web traffic. Competitive advantage is no longer won by simply having APIs; the key to gaining ground is based on the performance and the reliability of those APIs. This article presents a series of four case studies of how real time APIs were implemented.
By Karthik Krishnaswamy
MMS • Pat Helland
Article originally posted on InfoQ. Visit InfoQ
Wes Reisz talks to Pat Helland about the relationship between software architecture and urban planning. Pat explores planning for future growth, regulations/standards, and communication practices that cities–and software architecture–had to evolve to use. He uses these comparisons to distil lessons that architects can use in building distributed systems.
By Pat Helland
MMS • Ben Linders Scott Provence
Article originally posted on InfoQ. Visit InfoQ
The book Fail to Learn by Scott Provence explores how we can learn from failure and how trainers and course designers can use gamification to foster failure and learning in their educational environments. When playing games it’s ok to try out something, lose the game, learn from it, and restart and try something else.
By Ben Linders, Scott Provence